Model-supported allocation of vehicles to traffic lanes

ABSTRACT

A method as well as a device for the lane allocation of consecutive vehicles, the lane allocation being carried out in a model-based manner via a frequency distribution of the lateral displacements of detected radar objects. The method is additionally used for detecting the misalignment of the sensor.

FIELD OF THE INVENTION

The present invention relates to a method for the lane allocation ofconsecutive vehicles. In the past couple of years, numerous publicationshave become known that address an automatic regulation of the speed of avehicle while taking into consideration the distance to vehicles drivingahead. Such systems are often referred to as adaptive cruise control(ACC).

BACKGROUND INFORMATION

A fundamental description of such a device is included, for example, inthe paper “Adaptive Cruise Controls—System Aspects and DevelopmentTrends,” given by Winner, Witte et al., at SAE 96, Feb. 26 to 29, 1996in Detroit (SAE Paper No. 961010). To detect vehicles traveling aheadand stationary as well as moving objects, the majority of the knownsystems use a microwave radar beam or an infrared lidar beam. This beamis reflected by the objects and received by the sensor, thereby makingit possible to determine the relative position and relative speed of theobject. The future travel-path area of the vehicle can be predicted fromthis information, as is described in detail in German Patent No. DE 19722 947 C1.

SUMMARY OF THE INVENTION

It is an object of the present invention to make it possible to detect alane from reflected signals as well as to detect one's own lane oftravel, and, if applicable, to detect horizontal misalignment.Advantageously, the adaptive vehicle speed controller can be adjusted tomulti-lane roads because there are typically vehicles traveling insuccession on such roads. By lane detection as well as by detectingone's own lane of travel, the moving objects located in front of one'sown vehicle can be allocated to the appropriate lanes. By allocatingthese objects to the lanes, the target object traveling directly aheadcan be reliably determined, the target object's speed and accelerationdetermining the driving behavior of one's own, sensor-controlledvehicle. This lane allocation is carried out in that reference modelsfor roads having many different lanes as well as for the navigation ofthe different lanes are stored in a memory of the sensor. By inputtingthe acquired measured data in a lateral displacement histogram in whichthe frequency distribution of the lateral displacements of theindividual objects are entered, this instantaneous measuring diagram canbe correlated with the stored reference models. The reference modelhaving the greatest similarity to the instantaneous measuring diagramprovides information as to how many lanes the road has and in which lanethe vehicle is currently located. This result is output as a so-calledlane hypothesis. By evaluating the lateral displacements of thereflection objects as a function of their longitudinal distance, i.e.,the distance between the sensor and the reflection object, which extendsparallel to the center axis of the vehicle, a misalignment can bedetected. An advantage of the present invention is to output a lanehypothesis via this simple analysis method of sensor data and to detecta potentially existing sensor misalignment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of the model-based lane and misalignmentdetection.

FIG. 2 shows design approaches for the lane determination andmisalignment detection of the sensor.

DETAILED DESCRIPTION

It is essential for a frequency distribution of the lateraldisplacements of detected radar objects to be determined. A radar objectis an object confirmed, in each case, from one measurement to the nextmeasurement by comparing predicted distance, lateral displacement, andrelative speed data to ascertained measured data. The followingtreatments of the radar object data have proven to be effective: (a)initial filtering, i.e., every radar object is only taken intoconsideration once for the lateral displacement histogram; or (b)weighting the individual objects in the histogram as a function of thenumber of individual measurements of the individual radar objects.Displacement (dyv), which relates to the vehicle center, or, tocompensate for changes in lateral displacement due to cornering, lateraldisplacement (dyc), which relates to the course of the ACC vehicle, canbe used as the input quantity, lateral displacement. The determinedfrequency distribution is correlated to a model for frequencydistributions relating to lane allocation for multi-lane roads (e.g. 3lanes) having a defined width or, alternatively, to characteristiclateral displacement histograms for the different lanes used by the ACCvehicle. The model part having the highest correlation to the determinedfrequency distribution is output as the lane hypothesis (number of lanesand one's own lane of travel).

FIG. 1 shows a model-based lane and misalignment detection. In block 1of the flow chart, the radar object data, such as distance, relativespeed, and lateral displacement, are acquired from the measured data ofthe radar sensor. In a next step, these are filtered in an object filterrepresented as block 2. This filtering can be carried out in differentways. Advantageously, this is performed either in that every object isonly taken into consideration once for the lateral displacementhistogram or in that every object is taken into consideration with aweighting, the weighting being dependent upon how many times an objectwas detected in individual measurements. This filtered data is thenregistered in a lateral displacement histogram represented in block 3.The frequency of the filtered object data is stored in the lateraldisplacement histogram as a function of the measured lateraldisplacement of the vehicle's longitudinal axis. Lane models, which areused as reference histograms, are stored in block 4. These referencehistograms are either model-like lane models or empirically obtainedlane models. An individual, characteristic reference histogram is storedfor every type of road, whether it has oncoming traffic or not andwhether it has one or more lanes in one direction, and for the use ofeach lane. In block 5, the instantaneously determined, current lateraldisplacement histogram from block 3 is correlated to every referencemodel stored in block 4. The result of every correlation from theinstantaneous lateral displacement histogram to one of the referencemodels is a correlation result that increases as the similarity of theinstantaneous lateral displacement histogram to the reference histogramincreases. By selecting the reference histogram having the highestcorrelation result in block 5, the number of lanes, the used lane, aswell as a possible misalignment of the radar sensor can be deduced. Inblock 6, this acquired information is output and made available forfurther processing. The flow chart shown in FIG. 1 is arbitrarily runthrough many times, i.e., when a lane hypothesis and, if applicable, asensor misalignment are determined in block 6, the sequence begins againin that new radar data is processed in block 1 as described. Accordingto the number of detected lanes and their relative position with respectto one's own vehicle, a histogram having a plurality of maxima isobtained in block 3. The horizontal misalignment of the radar sensor canbe determined from the position of the average values for the lanes inthe histogram with respect to the vehicle center axis. For this purpose,in addition to lateral displacement dyv or, alternatively, dyc, afurther histogram regarding the distance of the observed object must bestored with equivalent object treatment (type (a) or (b)), and amisalignment angle must be determined by determining the centroid of thehistograms.

FIG. 2 shows a flow chart suitable for lane analysis and misalignmentdetection of a radar sensor. In block 7, it is detected whether thevehicle is on a straight road segment. A yaw rate signal coming, forexample, from a sensor for vehicle dynamics control can be used for thispurpose. Furthermore, it is conceivable to also take a steering angleinto consideration. If this yaw rate signal is less than 0.001 rad/s, itcan be concluded that the vehicle is traveling on a straight roadsegment. In this case, the amplitudes are filtered in block 8 in orderto only detect actual radar reflections and to eliminate noise. In block9, these measured points are represented in an x,y diagram. In block 10,the frequency with which the objects were detected by the radar beam canbe determined from the x,y diagram. In block 11, a distribution of thedetected objects on the road can be modeled from this x,y diagram bygenerating a lateral distribution histogram. The displacement of themodel produced in block 11 is then determined in block 12, thedisplacement making it possible to deduce the lateral deviation of one'sown vehicle in the lane of travel. In block 13, the instantaneouslateral displacement histogram is compared to the previous histogram. Alane hypothesis identifying the lane currently being used can be outputin block 14 by observing the changes in the data record in block 13. Ifit is detected in block 7 that the vehicle is on a straight roadsegment, the angle of the dominant object located in front of one's ownvehicle is determined in block 23. The dominant object is advantageouslythe vehicle traveling in the same lane as one's own vehicle and havingthe least distance to one's own vehicle, therefore being decisive forthe distance and speed control of one's own vehicle. Block 24 checkswhether the angle of the dominant object determined in block 23 isapproximately 0° as an average in time. If this condition of block 24 ismet, a verification of the current data using old data from previousmeasurements is carried out together with the frequencies from the x,ydiagram determined in block 10. If the current data is plausible on thebasis of the verification performed in block 25, this data is used inthe further course to determine a possible misalignment of the radarsensor in that this data is relayed to block 18. In block 19, an objectthat has been locked onto is then determined from the x, y diagram ofthe filtered objects determined in block 9. This locked object is avehicle traveling directly ahead, whose distance to one's own vehicleand whose relative speed in relation to one's own vehicle are used forthe distance and speed control. The position of this locked object isalso relayed to block 18 to determine a possible misalignment. At thesame time as step 19, the driving line centroids can be determined inblock 15 from the x,y diagram of block 9. These driving line centroidsrepresent the lateral displacement of the trajectories of vehiclesmoving in the middle of a particular lane. In block 17, it can bedetected from these driving line centroids whether the objects in theradar detection range are moving parallelly to one's own vehicle, whichis of particular interest for lane change maneuvers. Concurrently withthis step, the dominant object can be separately observed in block 16from the driving line centroids of step 15 and supplied to block 17, inthat it is detected whether the detected objects are moving in adirection parallel to one's own vehicle. The information acquired instep 17 regarding the parallelism of the detected objects is supplied tothe misalignment detection of the radar sensor in block 18. It isfurther advantageous in the case of a straight road segment detected inblock 7 to determine the parallel speeds from the present radar datasuch as angular velocity and relative speed, as shown in block 20. Theseparallel speeds are the speeds of the detected objects in relation toone's own vehicle. In block 21, the new positions of the detected radarobjects are then precalculated from these parallel speeds on the basisof the objects' old positions and trajectories. These precalculatedtargets are compared to the new measured data of the next measuringcycle and checked for plausibility. In step 22, a static centroid of thelateral displacements is determined from the data acquired in step 21,the centroid being supplied to block 18 and used there to determine apossible sensor misalignment. Block 26 then shows that a float angle ofthe vehicle is determined from the radar measurement. This is performedby monitoring the distances and the relative speeds of the radarobjects. In a further step in block 27, the float angle of the vehicleis determined via a further device. This is advantageously carried outby using driving dynamics quantities from a device for driving dynamicscontrol, which is already standard in most vehicles. The two floatangles calculated in steps 26 and 27 are compared to one another inblock 28, and a possibly existing difference between these twoquantities is relayed to the sensor misalignment detection in block 18.

The flow chart shown in FIG. 2 partially includes a plurality ofprocedures and design approaches for determining a quantity. Thus, thedetermination of a misalignment (18) using a plurality of possibilitiesis shown. According to the present invention, it is sufficient to use ineach case one of the indicated procedures to determine a lane or asensor misalignment. It is also conceivable to combine two or moreprocedures, the individual results being capable of being compared toone another and checked for plausibility.

1. A method for a motor vehicle having an adaptive distance and speedcontrol for lane allocation of vehicles on multi-lane roads, by using amodel-based lane and misalignment detection, the method comprising:acquiring radar object data from measured data of a radar sensor;filtering the radar object data by at least one of (i) considering onlyonce every object for a lateral displacement histogram, and (ii)considering every object with a weighting, the weighting depending uponhow many times an object was detected in individual measurements;registering the filtered data in a lateral displacement histogram, afrequency of the filtered object data being stored in the lateraldisplacement histogram as a function of the measured lateraldisplacement of the vehicle's longitudinal axis; correlating aninstantaneously determined, current lateral displacement histogram toevery stored reference lane model, wherein a result of every correlationfrom the instantaneous lateral displacement histogram to one of thereference lane models is a correlation result that increases as asimilarity of the instantaneous lateral displacement histogram increasesas to the reference histogram; selecting the reference histogram havinga highest correlation to determine acquired information, which includesa number of lanes, a used lane, and a possible misalignment of the radarsensor; and outputting the acquired information for processing.
 2. Themethod of claim 1, further comprising: obtaining a histogram having aplurality of maxima according to a number of detected lanes and theirrelative position with respect to the vehicle in the used lane;determining a horizontal misalignment of the radar sensor from aposition of average values for the lanes in the histogram with respectto a vehicle center axis, wherein in addition to a lateral displacement,a further histogram regarding a distance of an observed object is storedwith an equivalent object treatment, and a misalignment angle isdetermined by determining a centroid of the histograms.